import os
import logging
from typing import Any, Dict, Tuple

import mmcv
import h5py
import numpy as np
from nuscenes.map_expansion.map_api import NuScenesMap
from nuscenes.map_expansion.map_api import locations as LOCATIONS
from PIL import Image
import PIL.ImageDraw as ImageDraw


from mmdet.datasets.builder import PIPELINES
from mmdet.datasets.pipelines import LoadAnnotations

from ...core.points import BasePoints, get_points_type
from .loading_utils import load_augmented_point_cloud, reduce_LiDAR_beams
from .loading_utils import one_hot_decode


@PIPELINES.register_module()
class LoadMultiViewImageFromFiles:
    """Load multi channel images from a list of separate channel files.

    Expects results['image_paths'] to be a list of filenames.

    Args:
        to_float32 (bool): Whether to convert the img to float32.
            Defaults to False.
        color_type (str): Color type of the file. Defaults to 'unchanged'.
    """

    def __init__(self, to_float32=False, color_type="unchanged"):
        self.to_float32 = to_float32
        self.color_type = color_type

    def __call__(self, results):
        """Call function to load multi-view image from files.

        Args:
            results (dict): Result dict containing multi-view image filenames.

        Returns:
            dict: The result dict containing the multi-view image data. \
                Added keys and values are described below.

                - filename (str): Multi-view image filenames.
                - img (np.ndarray): Multi-view image arrays.
                - img_shape (tuple[int]): Shape of multi-view image arrays.
                - ori_shape (tuple[int]): Shape of original image arrays.
                - pad_shape (tuple[int]): Shape of padded image arrays.
                - scale_factor (float): Scale factor.
                - img_norm_cfg (dict): Normalization configuration of images.
        """
        filename = results["image_paths"]
        # img is of shape (h, w, c, num_views)
        # modified for waymo
        images = []
        h, w = 0, 0
        for name in filename:
            images.append(Image.open(name))
        
        #TODO: consider image padding in waymo

        results["filename"] = filename
        # unravel to list, see `DefaultFormatBundle` in formating.py
        # which will transpose each image separately and then stack into array
        results["img"] = images
        # [1600, 900]
        results["img_shape"] = images[0].size
        results["ori_shape"] = images[0].size
        # Set initial values for default meta_keys
        results["pad_shape"] = images[0].size
        results["scale_factor"] = 1.0
        
        return results

    def __repr__(self):
        """str: Return a string that describes the module."""
        repr_str = self.__class__.__name__
        repr_str += f"(to_float32={self.to_float32}, "
        repr_str += f"color_type='{self.color_type}')"
        return repr_str


@PIPELINES.register_module()
class LoadPointsFromMultiSweeps:
    """Load points from multiple sweeps.

    This is usually used for nuScenes dataset to utilize previous sweeps.

    Args:
        sweeps_num (int): Number of sweeps. Defaults to 10.
        load_dim (int): Dimension number of the loaded points. Defaults to 5.
        use_dim (list[int]): Which dimension to use. Defaults to [0, 1, 2, 4].
        pad_empty_sweeps (bool): Whether to repeat keyframe when
            sweeps is empty. Defaults to False.
        remove_close (bool): Whether to remove close points.
            Defaults to False.
        test_mode (bool): If test_model=True used for testing, it will not
            randomly sample sweeps but select the nearest N frames.
            Defaults to False.
    """

    def __init__(
        self,
        sweeps_num=10,
        load_dim=5,
        use_dim=[0, 1, 2, 4],
        pad_empty_sweeps=False,
        remove_close=False,
        test_mode=False,
        load_augmented=None,
        reduce_beams=None,
    ):
        self.load_dim = load_dim
        self.sweeps_num = sweeps_num
        if isinstance(use_dim, int):
            use_dim = list(range(use_dim))
        self.use_dim = use_dim
        self.pad_empty_sweeps = pad_empty_sweeps
        self.remove_close = remove_close
        self.test_mode = test_mode
        self.load_augmented = load_augmented
        self.reduce_beams = reduce_beams

    def _load_points(self, lidar_path):
        """Private function to load point clouds data.

        Args:
            lidar_path (str): Filename of point clouds data.

        Returns:
            np.ndarray: An array containing point clouds data.
        """
        mmcv.check_file_exist(lidar_path)
        if self.load_augmented:
            assert self.load_augmented in ["pointpainting", "mvp"]
            virtual = self.load_augmented == "mvp"
            points = load_augmented_point_cloud(
                lidar_path, virtual=virtual, reduce_beams=self.reduce_beams
            )
        elif lidar_path.endswith(".npy"):
            points = np.load(lidar_path)
        else:
            points = np.fromfile(lidar_path, dtype=np.float32)
        return points

    def _remove_close(self, points, radius=1.0):
        """Removes point too close within a certain radius from origin.

        Args:
            points (np.ndarray | :obj:`BasePoints`): Sweep points.
            radius (float): Radius below which points are removed.
                Defaults to 1.0.

        Returns:
            np.ndarray: Points after removing.
        """
        if isinstance(points, np.ndarray):
            points_numpy = points
        elif isinstance(points, BasePoints):
            points_numpy = points.tensor.numpy()
        else:
            raise NotImplementedError
        x_filt = np.abs(points_numpy[:, 0]) < radius
        y_filt = np.abs(points_numpy[:, 1]) < radius
        not_close = np.logical_not(np.logical_and(x_filt, y_filt))
        return points[not_close]

    def __call__(self, results):
        """Call function to load multi-sweep point clouds from files.

        Args:
            results (dict): Result dict containing multi-sweep point cloud \
                filenames.

        Returns:
            dict: The result dict containing the multi-sweep points data. \
                Added key and value are described below.

                - points (np.ndarray | :obj:`BasePoints`): Multi-sweep point \
                    cloud arrays.
        """
        points = results["points"]
        points.tensor[:, 4] = 0
        sweep_points_list = [points]
        ts = results["timestamp"] / 1e6
        if self.pad_empty_sweeps and len(results["sweeps"]) == 0:
            for i in range(self.sweeps_num):
                if self.remove_close:
                    sweep_points_list.append(self._remove_close(points))
                else:
                    sweep_points_list.append(points)
        else:
            if len(results["sweeps"]) <= self.sweeps_num:
                choices = np.arange(len(results["sweeps"]))
            elif self.test_mode:
                choices = np.arange(self.sweeps_num)
            else:
                # NOTE: seems possible to load frame -11?
                if not self.load_augmented:
                    choices = np.random.choice(
                        len(results["sweeps"]), self.sweeps_num, replace=False
                    )
                else:
                    # don't allow to sample the earliest frame, match with Tianwei's implementation.
                    choices = np.random.choice(
                        len(results["sweeps"]) - 1, self.sweeps_num, replace=False
                    )
            for idx in choices:
                sweep = results["sweeps"][idx]
                points_sweep = self._load_points(sweep["data_path"])
                points_sweep = np.copy(points_sweep).reshape(-1, self.load_dim)

                # TODO: make it more general
                if self.reduce_beams and self.reduce_beams < 32:
                    points_sweep = reduce_LiDAR_beams(points_sweep, self.reduce_beams)

                if self.remove_close:
                    points_sweep = self._remove_close(points_sweep)
                sweep_ts = sweep["timestamp"] / 1e6
                points_sweep[:, :3] = (
                    points_sweep[:, :3] @ sweep["sensor2lidar_rotation"].T
                )
                points_sweep[:, :3] += sweep["sensor2lidar_translation"]
                points_sweep[:, 4] = ts - sweep_ts
                points_sweep = points.new_point(points_sweep)
                sweep_points_list.append(points_sweep)

        points = points.cat(sweep_points_list)
        points = points[:, self.use_dim]
        results["points"] = points
        return results

    def __repr__(self):
        """str: Return a string that describes the module."""
        return f"{self.__class__.__name__}(sweeps_num={self.sweeps_num})"


@PIPELINES.register_module()
class LoadBEVSegmentation:
    '''This only loads map annotations, there is no dynamic objects.
    In this map, the origin is at lower-left corner, with x-y transposed.
                          FRONT                             RIGHT
         Nuscenes                       transposed
        --------->  LEFT   EGO   RIGHT  ----------->  BACK   EGO   FRONT
           map                            output
                    (0,0)  BACK                       (0,0)  LEFT
    Guess reason, in cv2 / PIL coord, this is a BEV as follow:
        (0,0)  LEFT

        BACK   EGO   FRONT

              RIGHT
    All masks in np channel first format.
    '''

    AUX_DATA_CH = {
        "visibility": 1,
        "center_offset": 2,
        "center_ohw": 4,
        "height": 1,
    }

    def __init__(
        self,
        dataset_root: str,
        xbound: Tuple[float, float, float],
        ybound: Tuple[float, float, float],
        classes: Tuple[str, ...],
        object_classes: Tuple[str, ...] = None,  # object_classes
        aux_data: Tuple[str, ...] = None,  # aux_data for dynamic objects
        cache_file: str = None,
    ) -> None:
        super().__init__()
        patch_h = ybound[1] - ybound[0]
        patch_w = xbound[1] - xbound[0]
        canvas_h = int(patch_h / ybound[2])
        canvas_w = int(patch_w / xbound[2])
        self.patch_size = (patch_h, patch_w)
        self.canvas_size = (canvas_h, canvas_w)
        self.classes = classes
        self.object_classes = object_classes
        self.aux_data = aux_data
        self.lidar2canvas = np.array([
            [canvas_h / patch_h, 0, canvas_h / 2],
            [0, canvas_w / patch_w, canvas_w / 2],
            [0, 0, 1]
        ])

        self.maps = {}
        for location in LOCATIONS:
            self.maps[location] = NuScenesMap(dataset_root, location)
        if cache_file and os.path.isfile(cache_file):
            logging.info(f"using data cache from: {cache_file}")
            # load to memory and ignore all possible changes.
            self.cache = cache_file
        else:
            self.cache = None
        # this should be set through main process afterwards
        self.shared_mem_cache = None

    def _get_dynamic_aux_bbox(self, aux_mask, data: Dict[str, Any]):
        '''Three aux data (7 channels in total), class-agnostic:
        1. visibility, 1-channel
        2. center-offset, 2-channel
        3. height/2, width/2, orientation, 4-channel, on bev canvas
        4. height of bbox, in lidar coordinate
        '''
        for _idx in range(len(data['gt_bboxes_3d'])):
            box = data['gt_bboxes_3d'][_idx]
            # get canvas coordinates
            # fmt:off
            _box_lidar = np.concatenate([
                box.corners[:, [0, 3, 7, 4], :2].numpy(),
                box.bottom_center[:, None, :2].numpy(),  # center
                box.corners[:, [4, 7], :2].mean(dim=1)[:, None].numpy(),  # front
                box.corners[:, [0, 4], :2].mean(dim=1)[:, None].numpy(),  # left
            ], axis=1)
            # fmt:on
            _box_canvas = np.dot(
                np.pad(_box_lidar, ((0, 0), (0, 0), (0, 1)), constant_values=1.0),
                self.lidar2canvas.T)[..., :2]  # N, 4, xy
            # in canvas coordinates
            box_canvas = _box_canvas[0, :4]
            center_canvas = _box_canvas[0, 4:5]
            front_canvas = _box_canvas[0, 5:6]
            left_canvas = _box_canvas[0, 6:7]
            # render mask
            render = Image.fromarray(np.zeros(self.canvas_size, dtype=np.uint8))
            draw = ImageDraw.Draw(render)
            draw.polygon(
                box_canvas.round().astype(np.int32).flatten().tolist(),
                fill=1)
            # construct
            tmp_mask = np.array(render) > 0
            coords = np.stack(np.meshgrid(
                np.arange(self.canvas_size[1]), np.arange(self.canvas_size[0])
            ), -1).astype(np.float32)
            _cur_ch = 0
            if "visibility" in self.aux_data:
                _ch_stop = _cur_ch + self.AUX_DATA_CH['visibility']
                aux_mask[tmp_mask, _cur_ch:_ch_stop] = data['visibility'][_idx]
                _cur_ch = _ch_stop
            if "center_offset" in self.aux_data:
                _ch_stop = _cur_ch + self.AUX_DATA_CH['center_offset']
                center_offset = coords[tmp_mask] - center_canvas
                aux_mask[tmp_mask, _cur_ch:_ch_stop] = center_offset
                _cur_ch = _ch_stop
            if "center_ohw" in self.aux_data:
                _ch_stop = _cur_ch + self.AUX_DATA_CH['center_ohw']
                height = np.linalg.norm(front_canvas - center_canvas)
                width = np.linalg.norm(left_canvas - center_canvas)
                # yaw = box.yaw  # scaling aspect ratio, yaw does not change
                v = ((front_canvas - center_canvas) / (
                    np.linalg.norm(front_canvas - center_canvas) + 1e-6))[0]
                # yaw = - np.arctan2(v[1], v[0])  # add negative, align with mmdet coord
                aux_mask[tmp_mask, _cur_ch:_ch_stop] = np.array([
                    height, width, v[0], v[1]])[None]
                _cur_ch = _ch_stop
            if "height" in self.aux_data:
                _ch_stop = _cur_ch + self.AUX_DATA_CH['height']
                bbox_height = box.height.item()  # in lidar coordinate
                aux_mask[tmp_mask, _cur_ch:_ch_stop] = np.array([
                    bbox_height])[None]
                _cur_ch = _ch_stop
        return aux_mask

    def _get_dynamic_aux(self, data: Dict[str, Any] = None) -> Any:
        '''aux data
        case 1: self.aux_data is None, return None
        case 2: data=None, set all values to zeros
        '''
        if self.aux_data is None:
            return None  # there is no aux_data

        aux_ch = sum([self.AUX_DATA_CH[aux_k] for aux_k in self.aux_data])
        if aux_ch == 0:  # there is no available aux_data
            if len(self.aux_data) != 0:
                logging.warning(f"Your aux_data: {self.aux_data} is not available")
            return None

        aux_mask = np.zeros((*self.canvas_size, aux_ch), dtype=np.float32)
        if data is not None:
            aux_mask = self._get_dynamic_aux_bbox(aux_mask, data)

        # transpose x,y and channel first format
        aux_mask = aux_mask.transpose(2, 1, 0)
        return aux_mask

    def _project_dynamic_bbox(self, dynamic_mask, data):
        '''We use PIL for projection, while CVT use cv2. The results are
        slightly different due to anti-alias of line, but should be similar.
        '''
        for cls_id, cls_name in enumerate(self.object_classes):
            # pick boxes
            cls_mask = data['gt_labels_3d'] == cls_id
            boxes = data['gt_bboxes_3d'][cls_mask]
            if len(boxes) < 1:
                continue
            # get coordinates on canvas. the order of points matters.
            bottom_corners_lidar = boxes.corners[:, [0, 3, 7, 4], :2]
            bottom_corners_canvas = np.dot(
                np.pad(bottom_corners_lidar.numpy(), ((0, 0), (0, 0), (0, 1)),
                       constant_values=1.0),
                self.lidar2canvas.T)[..., :2]  # N, 4, xy
            # draw
            render = Image.fromarray(dynamic_mask[cls_id])
            draw = ImageDraw.Draw(render)
            for box in bottom_corners_canvas:
                draw.polygon(
                    box.round().astype(np.int32).flatten().tolist(), fill=1)
            # save
            dynamic_mask[cls_id, :] = np.array(render)[:]
        return dynamic_mask

    def _project_dynamic(self, static_label, data: Dict[str, Any]) -> Any:
        """for dynamic mask, one class per channel
        case 1: data is None, set all values to zeros
        """
        # setup
        ch = len(self.object_classes)
        dynamic_mask = np.zeros((ch, *self.canvas_size), dtype=np.uint8)

        # if int, set ch=object_classes with all zeros; otherwise, project
        if data is not None:
            dynamic_mask = self._project_dynamic_bbox(dynamic_mask, data)

        # combine with static_label
        dynamic_mask = dynamic_mask.transpose(0, 2, 1)
        combined_label = np.concatenate([static_label, dynamic_mask], axis=0)
        return combined_label

    def _load_from_cache(
            self, data: Dict[str, Any], cache_dict) -> Dict[str, Any]:
        token = data['token']
        labels = one_hot_decode(
            cache_dict['gt_masks_bev_static'][token][:], len(self.classes))
        if self.object_classes is not None:
            if None in self.object_classes:
                # HACK: if None, set all values to zero
                # there is no computation, we generate on-the-fly
                final_labels = self._project_dynamic(labels, None)
                aux_labels = self._get_dynamic_aux(None)
            else:  # object_classes is list, we can get from cache_file
                final_labels = one_hot_decode(
                    cache_dict['gt_masks_bev'][token][:],
                    len(self.classes) + len(self.object_classes)
                )
                aux_labels = cache_dict['gt_aux_bev'][token][:]
            data["gt_masks_bev_static"] = labels
            data["gt_masks_bev"] = final_labels
            data["gt_aux_bev"] = aux_labels
        else:
            data["gt_masks_bev_static"] = labels
            data["gt_masks_bev"] = labels
        return data

    def _get_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        lidar2ego = data["lidar2ego"]
        ego2global = data["ego2global"]
        # lidar2global = ego2global @ lidar2ego @ point2lidar
        lidar2global = ego2global @ lidar2ego
        if "lidar_aug_matrix" in data:  # it is I if no lidar aux or no train
            lidar2point = data["lidar_aug_matrix"]
            point2lidar = np.linalg.inv(lidar2point)
            lidar2global = lidar2global @ point2lidar

        map_pose = lidar2global[:2, 3]
        patch_box = (
            map_pose[0], map_pose[1], self.patch_size[0], self.patch_size[1])

        rotation = lidar2global[:3, :3]
        v = np.dot(rotation, np.array([1, 0, 0]))
        yaw = np.arctan2(v[1], v[0])  # angle between v and x-axis
        patch_angle = yaw / np.pi * 180

        mappings = {}
        for name in self.classes:
            if name == "drivable_area*":
                mappings[name] = ["road_segment", "lane"]
            elif name == "divider":
                mappings[name] = ["road_divider", "lane_divider"]
            else:
                mappings[name] = [name]

        layer_names = []
        for name in mappings:
            layer_names.extend(mappings[name])
        layer_names = list(set(layer_names))

        # cut semantics from nuscenesMap
        location = data["location"]
        masks = self.maps[location].get_map_mask(
            patch_box=patch_box,
            patch_angle=patch_angle,
            layer_names=layer_names,
            canvas_size=self.canvas_size,
        )
        # masks = masks[:, ::-1, :].copy()
        masks = masks.transpose(0, 2, 1)  # TODO why need transpose here?
        masks = masks.astype(np.bool_)

        # here we handle possible combinations of semantics
        num_classes = len(self.classes)
        labels = np.zeros((num_classes, *self.canvas_size), dtype=np.int64)
        for k, name in enumerate(self.classes):
            for layer_name in mappings[name]:
                index = layer_names.index(layer_name)
                labels[k, masks[index]] = 1

        if self.object_classes is not None:
            data["gt_masks_bev_static"] = labels
            final_labels = self._project_dynamic(labels, data)
            aux_labels = self._get_dynamic_aux(data)
            data["gt_masks_bev"] = final_labels
            data["gt_aux_bev"] = aux_labels
        else:
            data["gt_masks_bev_static"] = labels
            data["gt_masks_bev"] = labels
        return data

    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
        # if set cache, use it.
        if self.cache is not None:
            try:
                with h5py.File(self.cache, 'r') as cache_file:
                    return self._load_from_cache(data, cache_file)
            except:
                pass
        if self.shared_mem_cache is not None:
            try:
                return self._load_from_cache(data, self.shared_mem_cache)
            except:
                pass

        # print("cache failed!")
        # cache miss, load normally
        data = self._get_data(data)

        # if set, add this item into it.
        if self.shared_mem_cache is not None:
            token = data['token']
            for key in self.shared_mem_cache.keys():
                if key in data:
                    self.shared_mem_cache[key][token] = data[key]
        return data


@PIPELINES.register_module()
class LoadPointsFromFile:
    """Load Points From File.

    Load sunrgbd and scannet points from file.

    Args:
        coord_type (str): The type of coordinates of points cloud.
            Available options includes:
            - 'LIDAR': Points in LiDAR coordinates.
            - 'DEPTH': Points in depth coordinates, usually for indoor dataset.
            - 'CAMERA': Points in camera coordinates.
        load_dim (int): The dimension of the loaded points.
            Defaults to 6.
        use_dim (list[int]): Which dimensions of the points to be used.
            Defaults to [0, 1, 2]. For KITTI dataset, set use_dim=4
            or use_dim=[0, 1, 2, 3] to use the intensity dimension.
        shift_height (bool): Whether to use shifted height. Defaults to False.
        use_color (bool): Whether to use color features. Defaults to False.
    """

    def __init__(
        self,
        coord_type,
        load_dim=6,
        use_dim=[0, 1, 2],
        shift_height=False,
        use_color=False,
        load_augmented=None,
        reduce_beams=None,
    ):
        self.shift_height = shift_height
        self.use_color = use_color
        if isinstance(use_dim, int):
            use_dim = list(range(use_dim))
        assert (
            max(use_dim) < load_dim
        ), f"Expect all used dimensions < {load_dim}, got {use_dim}"
        assert coord_type in ["CAMERA", "LIDAR", "DEPTH"]

        self.coord_type = coord_type
        self.load_dim = load_dim
        self.use_dim = use_dim
        self.load_augmented = load_augmented
        self.reduce_beams = reduce_beams

    def _load_points(self, lidar_path):
        """Private function to load point clouds data.

        Args:
            lidar_path (str): Filename of point clouds data.

        Returns:
            np.ndarray: An array containing point clouds data.
        """
        mmcv.check_file_exist(lidar_path)
        if self.load_augmented:
            assert self.load_augmented in ["pointpainting", "mvp"]
            virtual = self.load_augmented == "mvp"
            points = load_augmented_point_cloud(
                lidar_path, virtual=virtual, reduce_beams=self.reduce_beams
            )
        elif lidar_path.endswith(".npy"):
            points = np.load(lidar_path)
        else:
            points = np.fromfile(lidar_path, dtype=np.float32)

        return points

    def __call__(self, results):
        """Call function to load points data from file.

        Args:
            results (dict): Result dict containing point clouds data.

        Returns:
            dict: The result dict containing the point clouds data. \
                Added key and value are described below.

                - points (:obj:`BasePoints`): Point clouds data.
        """
        lidar_path = results["lidar_path"]
        points = self._load_points(lidar_path)
        points = points.reshape(-1, self.load_dim)
        # TODO: make it more general
        if self.reduce_beams and self.reduce_beams < 32:
            points = reduce_LiDAR_beams(points, self.reduce_beams)
        points = points[:, self.use_dim]
        attribute_dims = None

        if self.shift_height:
            floor_height = np.percentile(points[:, 2], 0.99)
            height = points[:, 2] - floor_height
            points = np.concatenate(
                [points[:, :3], np.expand_dims(height, 1), points[:, 3:]], 1
            )
            attribute_dims = dict(height=3)

        if self.use_color:
            assert len(self.use_dim) >= 6
            if attribute_dims is None:
                attribute_dims = dict()
            attribute_dims.update(
                dict(
                    color=[
                        points.shape[1] - 3,
                        points.shape[1] - 2,
                        points.shape[1] - 1,
                    ]
                )
            )

        points_class = get_points_type(self.coord_type)
        points = points_class(
            points, points_dim=points.shape[-1], attribute_dims=attribute_dims
        )
        results["points"] = points

        return results


@PIPELINES.register_module()
class LoadAnnotations3D(LoadAnnotations):
    """Load Annotations3D.

    Load instance mask and semantic mask of points and
    encapsulate the items into related fields.

    Args:
        with_bbox_3d (bool, optional): Whether to load 3D boxes.
            Defaults to True.
        with_label_3d (bool, optional): Whether to load 3D labels.
            Defaults to True.
        with_attr_label (bool, optional): Whether to load attribute label.
            Defaults to False.
        with_bbox (bool, optional): Whether to load 2D boxes.
            Defaults to False.
        with_label (bool, optional): Whether to load 2D labels.
            Defaults to False.
        with_mask (bool, optional): Whether to load 2D instance masks.
            Defaults to False.
        with_seg (bool, optional): Whether to load 2D semantic masks.
            Defaults to False.
        with_bbox_depth (bool, optional): Whether to load 2.5D boxes.
            Defaults to False.
        poly2mask (bool, optional): Whether to convert polygon annotations
            to bitmasks. Defaults to True.
    """

    def __init__(
        self,
        with_bbox_3d=True,
        with_label_3d=True,
        with_attr_label=False,
        with_bbox=False,
        with_label=False,
        with_mask=False,
        with_seg=False,
        with_bbox_depth=False,
        poly2mask=True,
    ):
        super().__init__(
            with_bbox,
            with_label,
            with_mask,
            with_seg,
            poly2mask,
        )
        self.with_bbox_3d = with_bbox_3d
        self.with_bbox_depth = with_bbox_depth
        self.with_label_3d = with_label_3d
        self.with_attr_label = with_attr_label

    def _load_bboxes_3d(self, results):
        """Private function to load 3D bounding box annotations.

        Args:
            results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

        Returns:
            dict: The dict containing loaded 3D bounding box annotations.
        """
        results["gt_bboxes_3d"] = results["ann_info"]["gt_bboxes_3d"]
        results["bbox3d_fields"].append("gt_bboxes_3d")
        return results

    def _load_bboxes_depth(self, results):
        """Private function to load 2.5D bounding box annotations.

        Args:
            results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

        Returns:
            dict: The dict containing loaded 2.5D bounding box annotations.
        """
        results["centers2d"] = results["ann_info"]["centers2d"]
        results["depths"] = results["ann_info"]["depths"]
        return results

    def _load_labels_3d(self, results):
        """Private function to load label annotations.

        Args:
            results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

        Returns:
            dict: The dict containing loaded label annotations.
        """
        results["gt_labels_3d"] = results["ann_info"]["gt_labels_3d"]
        return results

    def _load_attr_labels(self, results):
        """Private function to load label annotations.

        Args:
            results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

        Returns:
            dict: The dict containing loaded label annotations.
        """
        results["attr_labels"] = results["ann_info"]["attr_labels"]
        return results

    def __call__(self, results):
        """Call function to load multiple types annotations.

        Args:
            results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

        Returns:
            dict: The dict containing loaded 3D bounding box, label, mask and
                semantic segmentation annotations.
        """
        results = super().__call__(results)
        if self.with_bbox_3d:
            results = self._load_bboxes_3d(results)
            if results is None:
                return None
        if self.with_bbox_depth:
            results = self._load_bboxes_depth(results)
            if results is None:
                return None
        if self.with_label_3d:
            results = self._load_labels_3d(results)
        if self.with_attr_label:
            results = self._load_attr_labels(results)

        return results
